Testing the volatility jumps based on the high frequency data

This article tests volatility jumps based on the high frequency data. Under the null hypothesis that the volatility process is a continuous semimartingale, our test statistic converges to a normal distribution, and under the alternative hypothesis where the volatility has jumps, the statistic diverg...

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Published in:Journal of time series analysis 2022-09, Vol.43 (5), p.669-694
Main Authors: Liu, Guangying, Liu, Meiyao, Lin, Jinguan
Format: Article
Language:English
Subjects:
Central limit theorem
Empirical analysis
High frequencies
high frequency data
Infinity
Normal distribution
Null hypothesis
semimartingale
Simulation
Statistical analysis
Volatility
Volatility jumps
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description This article tests volatility jumps based on the high frequency data. Under the null hypothesis that the volatility process is a continuous semimartingale, our test statistic converges to a normal distribution, and under the alternative hypothesis where the volatility has jumps, the statistic diverges to infinity. Compared to the test statistic of Bibinger et al. (Bibinger et al. (2017). Annals of Statistics 45, 1542–1578), our proposed statistic diverges to infinity at a faster rate, and has a better power. Simulation studies confirm the theoretical results, and an empirical analysis shows that some real financial data possess volatility jumps.
doi_str_mv 10.1111/jtsa.12634
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source International Bibliography of the Social Sciences (IBSS); Wiley-Blackwell Read & Publish Collection
subjects Central limit theorem
Empirical analysis
High frequencies
high frequency data
Infinity
Normal distribution
Null hypothesis
semimartingale
Simulation
Statistical analysis
Volatility
Volatility jumps
title Testing the volatility jumps based on the high frequency data
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